AI and Machine Learning: Can We Build an Artificial Brain?

AI is changing the world around us, making its way into businesses, health care, science and many other areas. In fact, most of us enjoy working with the omniscient Google, the world's largest AI, on a daily basis already. Google; Golem; God: the associations are quite imposing. Are we on the verge of creating a truly artificial brain?

When artificial intelligence first emerged as a discipline, scientists had great hopes for it. They wanted to create General Artificial Intelligence, that is, a computer system capable of doing anything a human can, in a better and faster way than we do. After AI failed to deliver on its initial promises, scientists have scaled back expectations, focusing instead on specific tasks.

This is called Narrow AI, and even though it’s a step back from General AI, it still gets important jobs done. Today’s software won’t argue with you about the world economy while making you a cup of tea or make you feel better when you’re depressed. But it can still recognize your face and understand you when you invite it to a game of chess.

Today, after years of trying different approaches to creating AI, "machine learning" is the only area that yields promising and relevant results. The idea behind it is fairly simple. Instead of programming computers with a specific set of instructions to perform a particular task, such as moving, speaking, or recognizing faces, you code machines to learn how to perform the task on their own.

Unlike traditional programming, which uses explicit, sequential instructions, machine learning software looks at a many sample data and uses statistical modeling to find patterns in it. Training it to recognize images of horses involves showing it lots of horse photos tagged as such, along with another set of images of other things. The machine then learns which data points are common to horse images, and can use them to identify new images.

Several algorithmic concepts have been used for machine learning, but it was biomimetics, or biomimicry, that allowed for a real breakthrough. Biomimetics takes inspiration from biology, in this case the human brain, in order to design a more intelligent machine. This led to the development of artificial neural networks, which are programmed to process information in the same way that our brains do.

Due to the exponential increase in computing power in recent years, we can now build neural networks with much larger and deeper layers than it was previously possible. Although there is no clear boundary between the terms, this area of machine learning is often described as "deep learning."

Deep learning can be summarized as a system of probability with a feedback loop on top. It can make statements with some certainty on the basis of a large data set. For example, the system might be 77% confident that there is a horse in the picture, 90% certain that it’s an animal and 12% certain it’s a toy. To improve itself, the artificial neural network can learn from its mistakes, in order to make a better decision later on.

Our own learning processes are linked to the synapses in the brain, which serve as connections between our neurons. The more a synapse is stimulated, the more the connection is reinforced and learning is enhanced. Researchers took inspiration from this mechanism to design an artificial synapse called a memristor.

The resistance of this electronic nanocomponent can be tuned using voltage pulses similar to those in neurons. If the resistance is low, the synaptic connection will be strong, and if the resistance is high, the connection will be weak. This ability to adjust its resistance enables the synapse to learn. However, aside from the problem of not really understanding how human intelligence works, the functioning of a biological system like our brain remains fundamentally different.

Initial enthusiasm about AI has faded and the sci-fi scenarios are largely over. Even with the emergence of new machine learning techniques, the field's ultimate goal—some form of General AI—remains a distant vision. Still, powerful machine learning is expanding into new industries and areas of everyday life and will increase attention to the unintended consequences that may ensue.